Tracking of an object entails locating the object's position at successive instances with the object manually defined in the first frame or as the output of an object detector. In general, object tracking depends on extracting one or more characteristic features of the object (motion, color, shape, appearance) and using such characteristic feature(s) to estimate the position of the object in a next image frame, based on the object's position in the current image frame. A number of techniques exist for object tracking, including, optimal filtering, point-tracking, tracking-by-detection, optical-flow, and background subtraction, for example.
Proposals to refine object tracking have suggested gaining an advantage by modeling the foreground (the tracked object) and the background, and using this information to reject the estimated object positions most likely belonging to the background. The basic approach to such modeling entails extracting a model of the background appearance using color distributions learned in the first frame, for example, and updating such distributions along the sequence of images. However, such modeling requires prior knowledge of the background and the object, in order to learn the appearance of the object correctly. For this reason, foreground/background segmentation has become a key component in recent top-performing tracking devices. Moreover, even with correct initialization of the segmentation between the object and the background, present-day models often do not sufficiently discriminate between the object and the background for rigorously tracking the object. Finally, complete or partial occlusions of the object and changes in the appearance of the object resulting from rotation, illumination, shadows, and/or self-occlusions, for example, increase the difficulty of establishing a successful model adaptation strategy.
Thus, a need exists for a technique for object tracking that overcomes the foregoing disadvantages of the prior art.